From the results of Table REF , we can summarize several observations: 1) The mutual information (KL-divergence) plays a strong role in learning latent representation since our model (U-S-VAE 1L) can achieve better performance than P&C [1]} on two datasets with cross-subject protocol. 2) The score of P&C in cross-view evaluation is higher than ours, this is because P&C has a pre-processing step to implement a view-invariant transformation [1]}. Besides, P&C also has an additional feature-level auto-encoder can benefit the classification. 3) In ablation studies, U-S-VAE 1L can achieve the best performance on both iMiGUE and NTU RGB+D datasets. U-S-VAE with hidden states \(Z_T\)  can beat U-S-VAE with the cell states (U-S-VAE 1L w C).
